ReviewEBioMedicine2022
Challenging molecular dogmas in human sepsis using mathematical reasoning.
Review in EBioMedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Extracellular Vesicle-Derived MicroRNAs' Value in Diagnosing and Predicting Clinical Outcomes in Patients with COVID-19 and Bacterial Sepsis.International journal of molecular sciences · 2026Article
- Assessment of disease severity in hospitalized community-acquired pneumonia by the use of validated scoring systems.BMC pulmonary medicine · 2025Article
- Conventional and unconventional T-cell responses contribute to the prediction of clinical outcome and causative bacterial pathogen in sepsis patients.Clinical and experimental immunology · 2024Article
- Article
- Article
- Pneumonia in the first week after polytrauma is associated with reduced blood levels of soluble herpes virus entry mediator.Frontiers in immunology · 2023Article
- Integrated genomic analysis defines molecular subgroups in dilated cardiomyopathy and identifies novel biomarkers based on machine learning methods.Frontiers in genetics · 2023Article
- Review
- Artificial intelligence-assisted phenotyping of sepsis: Research progress, clinical challenges, and translational prospects.Digital healthReview
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis is defined as a dysregulated host-response to infection, across all ages and pathogens. What defines a dysregulated state remains intensively researched but incompletely understood. Here, we dissect the meaning of this definition and its importance for the diagnosis and management of sepsis. We deliberate on pathophysiological features and dogmas that range from cytokine storms and immune paralysis to dormancy and altered homeostasis setpoints. Mathematical reasoning, used to test for plausibility, reveals three interlinked cardinal rules governing host-response trajectories in sepsis. Rule one highlights that the amplitude of the immune response while important is not sufficient and is strictly dependent on rule two, specifying bioenergetic capacity and are together dynamically driven by rule three, delineating stability and alterations in setpoints. We consider these rules and associated pathophysiological parameters for guiding data-science and artificial intelligence mining of multi-omics and big-data for improving the precision of diagnostic and therapeutic approaches to sepsis.
fundingPG funded by the European Regional Development Fund and Welsh Government (Ser Cymru programme - Project Sepsis).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.